PROJECT ABSTRACT Due to modern transportation, the world is more connected than ever before, allowing infectious diseases to spread globally at unprecedented rates. Influenza, which causes yearly epidemics as well as sporadic and severe pandemics, is of particular concern. Previously, models capable of accurately forecasting influenza activity have been developed; however, systems capable of forecasting influenza transmission on the global scale are few, and have not been optimized for use in real time. Here, we propose to develop a global model of influenza transmission, and assess its ability to accurately forecast timing of influenza onset, timing of peak influenza activity, and influenza prevalence at the epidemic peak at the country scale. In Aim 1, we use a previously developed and validated model-data assimilation system to generate retrospective forecasts of influenza activity at the individual country level. In Aim 2, we expand the model to incorporate air travel, as well as short-distance, non-air travel, between countries, and assess the quality of model fit to observed influenza data. Aim 3 will use this global model to describe the relative impact of long-distance vs. short-distance international travel. Finally, in Aim 4, we use the newly developed global model to retrospectively forecast international influenza patterns, and compare the accuracy of the resulting forecasts to the country-level forecasts obtained in Aim 1. This project will result in a global model capable of accurately forecasting international spread of influenza, as well as an improved understanding of how different modes of transportation impact spatial patterns of influenza transmission. Accurate global forecasts will, in turn, allow public health officials, medical professionals, and the general public alike to better prepare for periods of increased influenza activity, reducing morbidity and mortality both yearly and in the case of pandemic emergence.